moabb 1.4.1

Mother of All BCI Benchmarks

Mother of all BCI Benchmarks Build a comprehensive benchmark of popular Brain-Computer Interface (BCI) algorithms applied on an extensive list of freely available EEG datasets.

Disclaimer

This is an open science project that may evolve depending on the need of the community.

The problem

Brain-Computer Interfaces allow to interact with a computer using brain signals. In this project, we focus mostly on electroencephalographic signals (EEG), that is a very active research domain, with worldwide scientific contributions. Still:

• Reproducible Research in BCI has a long way to go.
• While many BCI datasets are made freely available, researchers do not publish code, and reproducing results required to benchmark new algorithms turns out to be trickier than it should be.
• Performances can be significantly impacted by parameters of the preprocessing steps, toolboxes used and implementation “tricks” that are almost never reported in the literature.

As a result, there is no comprehensive benchmark of BCI algorithms, and newcomers are spending a tremendous amount of time browsing literature to find out what algorithm works best and on which dataset.

The solution

The Mother of all BCI Benchmarks allows to:

• Build a comprehensive benchmark of popular BCI algorithms applied on an extensive list of freely available EEG datasets.
• The code is available on GitHub, serving as a reference point for the future algorithmic developments.
• Algorithms can be ranked and promoted on a website, providing a clear picture of the different solutions available in the field.

This project will be successful when we read in an abstract “ … the proposed method obtained a score of 89% on the MOABB (Mother of All BCI Benchmarks), outperforming the state of the art by 5% ...”.

Core Team

This project is under the umbrella of NeuroTechX, the international community for NeuroTech enthusiasts.

The project is currently maintained by:

Sylvain Chevallier	Bruno Aristimunha	Igor Carrara	Pierre Guetschel

The Mother of all BCI Benchmarks was founded by Alexander Barachant and Vinay Jayaram, who are experts in the field of Brain-Computer Interfaces (BCI). At the moment, both work as Research Scientists at Meta.

Alexander Barachant	Vinay Jayaram

Contributors

The MOABB is a community project, and we are always thankful to all the contributors!

<script> const endpoint = 'https://api.github.com/repos/NeuroTechX/moabb/contributors'; const container = document.getElementById('contributors-container'); const filterList = ["bruAristimunha", "sylvchev", "carraraig", "pierreGtch", "sara04", "pre-commit-ci[bot]", "dependabot[bot]", "alexandrebarachant", "vinay-jayaram"]; fetch(endpoint) .then(response => response.json()) .then(contributors => { const filteredContributors = contributors.filter(contributor => !filterList.includes(contributor.login)); filteredContributors.forEach(contributor => { const link = document.createElement('a'); link.href = contributor.html_url; link.target = '_blank'; const img = document.createElement('img'); img.src = contributor.avatar_url; img.alt = contributor.login; img.style.width = '100px'; img.style.height = '100px'; img.style.objectFit = 'cover'; img.style.borderRadius = '50%'; link.appendChild(img); container.appendChild(link); }); }); </script>

Special acknowledge for the extra MOABB contributors:

Pedro Rodrigues

What do we need?

You! In whatever way you can help.

We need expertise in programming, user experience, software sustainability, documentation and technical writing and project management.

We'd love your feedback along the way.

Our primary goal is to build a comprehensive benchmark of popular BCI algorithms applied on an extensive list of freely available EEG datasets, and we're excited to support the professional development of any and all of our contributors. If you're looking to learn to code, try out working collaboratively, or translate your skills to the digital domain, we're here to help.

Citing MOABB and related publications

If you use MOABB in your experiments, please cite this library when publishing a paper to increase the visibility of open science initiatives:

• Here is the APA version:

Aristimunha, B., Carrara, I., Guetschel, P., Sedlar, S., Rodrigues, P., Sosulski, J., Narayanan, D., Bjareholt, E., Barthelemy, Q., Schirrmeister, R. T., Kobler, R., Kalunga, E., Darmet, L., Gregoire, C., Abdul Hussain, A., Gatti, R., Goncharenko, V., Thielen, J., Moreau, T., Roy, Y., Jayaram, V., Barachant, A., & Chevallier, S. (2025).
Mother of all BCI Benchmarks (MOABB), 2025. DOI: 10.5281/zenodo.10034223.

and the Bibtex version:

    @software{Aristimunha_Mother_of_all,
      author       = {Aristimunha, Bruno and
                      Carrara, Igor and
                      Guetschel, Pierre and
                      Sedlar, Sara and
                      Rodrigues, Pedro and
                      Sosulski, Jan and
                      Narayanan, Divyesh and
                      Bjareholt, Erik and
                      Barthelemy, Quentin and
                      Schirrmeister, Robin Tibor and
                      Kobler, Reinmar and
                      Kalunga, Emmanuel and
                      Darmet, Ludovic and
                      Gregoire, Cattan and
                      Abdul Hussain, Ali and
                      Gatti, Ramiro and
                      Goncharenko, Vladislav and
                      Thielen, Jordy and
                      Moreau, Thomas and
                      Roy, Yannick and
                      Jayaram, Vinay and
                      Barachant, Alexandre and
                      Chevallier, Sylvain},
            title        = {Mother of all BCI Benchmarks},
            year         = 2025,
            publisher    = {Zenodo},
            version      = {v1.2.0},
            url = {https://github.com/NeuroTechX/moabb},
            doi = {10.5281/zenodo.10034223},
    }

If you want to cite the scientific contributions of MOABB, you could use the following paper:

Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Junqueira Lopes, Sébastien Velut, Salim Khazem, Thomas Moreau "The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark" HAL: hal-04537061.

Vinay Jayaram and Alexandre Barachant. "MOABB: trustworthy algorithm benchmarking for BCIs." Journal of neural engineering 15.6 (2018): 066011. DOI

If you publish a paper using MOABB, please contact us on gitter or open an issue! We would love to hear about your work and help you promote it.

Contact us

If you want to report a problem or suggest an enhancement, we'd love for you to open an issue at this GitHub repository because then we can get right on it.

For a less formal discussion or exchanging ideas, you can also reach us on the Github or join our weekly office hours! This an open video meeting happening on a regular basis, please ask the link on the gitter channel. We are also on NeuroTechX Slack channel #moabb.